Transmission of a high quality video image over a low band-width channel, such as a standard telephone line, is time consuming. The relatively low band-width of a telephone line, and the large band-width of a high quality video image, can dictate transmission times for a single analog video frame on the order of 20 minutes. It has been recognized that one way around such unacceptably long transmission times for a single video frame is to digitize and compress the video signal prior to transmission. Various image data compression techniques are known in the art, including thresholding, normalization, quantization and minimum redundancy encoding.
Thresholding discards data words of magnitudes less than a threshold number. Normalization entails dividing each data word by a divisor to yield a quotient. Quantization discards the fractional bits in the quotient. Minimum redundancy encoding is a technique well-known in the prior art and employs two complimentary steps, namely amplitude encoding and run length encoding. Amplitude encoding (or "Huffman Encoding") simply assigns to each of a finite set of possible amplitudes an encoded bit pattern designed to require the smallest number of bits for non-redundant representation. Run length encoding simply represents any consecutive run of zeros in the data as the smallest non-redundant bit pattern required to count the number of zeros in the run. The set of bit patterns representing each of the possible word amplitudes and the set of bit patterns representing each of the possible zero run lengths may be selected in accordance with the well-known principles and stored in look-up tables for use during the compression process, and need not be described further herein.
It is well-known that compression techniques are greatly enhanced when applied to image data which has been previously transformed in accordance with a discrete cosine transform algorithm. Discrete cosine transforms are well-known in the art of image data compression. They are presently preferred above all other species of transforms because, for a given number of resultant transform coefficients, the variance of the inverse transformed data from its original is smallest when a discrete cosine transform is employed, and is larger whenever any other type of transform is employed for which a discrete algorithm is known, such as a fast-Fourier transform, etc.
These compression techniques greatly reduce the number of bits required to represent a frame of video information, without a proportionate reduction in image quality, thereby reducing the amount of time required to transmit a single video frame.